Generally speaking, logistical processes increase efficiency and reduce cost of commerce in relation to storing inventory and transporting cargo. For example, storage space is finite and transport media, such as trailers, have specified capacities. Logistic processing apportions cargoes and inventories efficiently over the available spaces, which can facilitate storage and expedite transport.
To apportion a cargo or inventory, dimensions of each of the constituent packages, boxes, crates and other items (“items”) are measured. The measured dimensions are processed in relation to the available storage or transport space. Based on the processing, a position within the storage/transport space is computed that optimizes placement of each inventory/cargo item relative to each of the other items.
The measuring of the dimensions of the cargo/inventory items may be automated by a dimensioning apparatus (“dimensioner”), which may be operable optically. Optically based dimensioners are typically operable for capturing image data using photographic and/or videographic techniques. Image data captured in relation to surfaces of the cargo/inventory items are used for computing the measurements.
Dimensioners capture the image data over two or more measurably sufficient (“good”) surfaces of the cargo/inventory items to produce measurements with levels of accuracy sufficient for commercial application. Use of three good surfaces may improve measurement accuracy for commerce. In some situations, however, dimensioners may sometimes capture substantially inaccurate (“false”) image data.
Computations based on the false captured image data produce inaccurate measurements of the dimensions of the items, which can cause faulty cargo/inventory apportioning. Excessive false image value production levels are thus unacceptable with dimensioners certified for commercial use, e.g., under the National Type Evaluation Program (NTEP) of the (U.S.) National Council on Weights and Measures.
On the contrary, NTEP certified dimensioners rely on consistently reliable measurement accuracy and thus, in the image values on which the measurements are based. Dimensioners may be deployed in industrial settings, e.g., in which they capture the image data from cargo/inventory items as the items are moved on high speed conveyors. Such usage however may sometimes degrade the accuracy of image based measurements.
For example, images captured by the dimensioner from an item that is beyond an optical range limit may lack sufficient structured light information for accurate measurement. Even with sufficient structured light information, accuracy may be affected by an orientation of an item relative to the dimensioner. For example, a dimensioner oriented straight-on to one face of an item may measure its depth inaccurately.
Therefore, a need exists for evaluating image data, captured from items examined by dimensioners, in relation to suitability of the data for computing accurate dimension measurements therewith. A need also exists for recognizing false values in the image data captured by the dimensioners and rejecting use of the false values in computing dimension measurements. Further, a need exists for recommending and/or implementing corrections in relation to the false image data, in order to produce accurate dimension measurements.